A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

Computers in Biology and Medicine

Volumn 40, Issue 3, 2010, Pages 318-330

  Soria, Daniele ^a   Garibaldi, Jonathan M ^a   Ambrogi, Federico ^c   Green, Andrew R ^b   Powe, Des ^b   Rakha, Emad ^b   Douglas Macmillan, R ^f   Blamey, Roger W ^f   Ball, Graham ^d   Lisboa, Paulo J G ^e   Etchells, Terence A ^e   Boracchi, Patrizia ^c   Biganzoli, Elia ^c   Ellis, Ian O ^b  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b NOTTINGHAM UNIVERSITY HOSPITALS NHS TRUST   (United Kingdom)

^c UNIVERSITY OF MILAN   (Italy)

^d NOTTINGHAM TRENT UNIVERSITY   (United Kingdom)

^e LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^f NOTTINGHAM CITY HOSPITAL   (United Kingdom)

Author keywords

Breast cancer; Clustering methods; Consensus clustering; Molecular classification; Validity indices

Indexed keywords

BREAST CANCER; CLUSTERING METHODS; CONSENSUS CLUSTERING; MOLECULAR CLASSIFICATION; VALIDITY INDEX;

CLUSTERING ALGORITHMS;

ADULT; AGED; ALGORITHM; ANALYTIC METHOD; ARTICLE; BREAST CANCER; CLINICAL EVALUATION; CLUSTER ANALYSIS; CONSENSUS; CONTROLLED STUDY; FUZZY SYSTEM; HUMAN; IMMUNOHISTOCHEMISTRY; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MATHEMATICAL MODEL; PRIORITY JOURNAL; STATISTICAL PARAMETERS; VALIDATION PROCESS;

ALGORITHMS; BREAST NEOPLASMS; CLUSTER ANALYSIS; FEMALE; HUMANS; IMMUNOHISTOCHEMISTRY;

EID: 77549085806     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2010.01.003     Document Type: Article

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